首页|基于GA-BP神经网络的316L多层多道焊残余应力和变形预测

基于GA-BP神经网络的316L多层多道焊残余应力和变形预测

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  • NETL
  • NSTL
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残余应力与变形是焊接过程中普遍存在的现象,对焊接结构的性能及使用寿命产生严重影响,是焊接结构发生开裂和失效的主要原因之一.传统的多层多道焊残余应力和变形预测主要采用有限元分析法,该方法存在预测精度差,数值模拟结果可靠性低等缺点.针对预测 20 mm厚 316L平板对接焊的残余应力和变形,提出一种基于遗传算法(genetic algorithm,GA)优化的反向传播(back propagation,BP)神经网络预测模型GA-BP,选取最主要的 4个焊接工艺参数作为输入,包括焊接电流、电弧电压、焊接速度和层间温度,以焊后最大横、纵向残余应力和变形作为输出.结果表明,BP神经网络模型的预测误差在 15%以内,优化后的GA-BP网络模型预测误差小于 3%,故GA-BP神经网络模型的预测更精准,该方法可为多层多道焊的焊接工艺参数优化以及焊后残余应力与变形的预测和控制提供思路与理论基础,具有一定的指导意义和实际应用价值.
Prediction of residual stress and deformation of 316L multi-layer multi-pass welding based on GA-BP neural network
Residual stress and deformation are common phenomena in the welding process.Its existence will have a serious impact on the working performance and service life of the welded structure,and is one of the main reasons for the cracking and failure of the welded structure.Traditional methods for predicting residual stress and deformation mainly include finite element analysis.However,these methods have the disadvantages of poor prediction accuracy and low reliability of numerical simulation results.To address the problem of predicting residual stress and deformation in 316L flat plates welding with a thickness of 20 mm,this paper proposes a GA-BP neural network prediction model based on optimized back propagation(BP)by genetic algorithm(GA),which selects the four most important welding process parameters as input parameters,including welding current,electric arc voltage,welding speed,and interpas temperature.The output of the model is the maximum transverse and longitudinal residual stress and deformation after welding.The results show that the error of the BP neural network model is within 15%.The error of GA-BP is less than 3%,indicating that the GA-BP neural network model is more accurate.This method can provide ideas and theoretical basis for optimizing process parameters of multi-layer multi-pass welding,as well as predicting and controlling residual stress and deformation after welding,and has certain practical guidance and application value.

genetic algorithmBP neural networkwelding process parameterswelding residual stresswelding deformation

李成文、吉海标、闫朝辉、刘志宏、马建国、王锐、吴杰峰

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中国科学技术大学,合肥,230026

中国科学院合肥物质科学研究院,等离子体物理研究所,合肥,230031

合肥综合性国家科学中心能源研究院,合肥,230051

淮南新能源研究中心,特种焊接技术安徽省重点实验室,淮南,232063

合肥聚能电物理高技术开发有限公司,合肥,230031

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遗传算法 BP神经网络 焊接工艺参数 焊接残余应力 焊接变形

国家重大科技基础设施建设项目安徽省自然科学基金中国科学院青年促进会资助项目国家自然科学基金青年基金

2018-000052-73-01-0012282108085ME142201943312105185

2024

10.12073/j.hjxb.20230520002

焊接学报
中国机械工程学会　中国机械工程学会焊接学会　机械科学研究院哈尔滨焊接研究所

焊接学报

CSTPCD北大核心
影响因子：0.815
ISSN：0253-360X
年,卷(期)：2024.45(5)
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